Multifunctional therapeutic massage bed and control system

Abstract

The invention discloses a multifunctional health-care massage bed and a control system, and belongs to the field of intelligent control, and the multifunctional health-care massage bed comprises a data monitoring module which is used for collecting and obtaining a health monitoring data flow of a target user; the scheme analysis module is used for determining a target physiotherapy scheme; the multifunctional execution module is used for obtaining a target function control parameter scheme and executing massage monitoring on a target user to obtain massage monitoring feedback parameters; and the early warning optimization module is used for performing health index evaluation on the massage monitoring feedback parameters, acquiring user health index information and performing massage early warning optimization control on the target user. The technical problems that an existing health care massage bed is single in function and cannot provide a personalized physiotherapy scheme according to the physical condition of the user are solved, and the technical effects that the personalized physiotherapy scheme is intelligently determined according to the health monitoring data flow of the user, and meanwhile real-time monitoring and early warning optimization feedback in the massage process are achieved are achieved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent control, and particularly to a multifunctional health massage bed and a control system. Background Art

[0002] Existing health massage beds have certain limitations in function and cannot provide personalized physiotherapy solutions according to the actual physical conditions of users. Although some health massage beds have health monitoring functions, they are limited to simple data collection and display, lacking the ability of intelligent data analysis and formulation of physiotherapy solutions. In addition, existing health massage beds lack a real-time monitoring and dynamic adjustment feedback mechanism during the massage process and cannot optimize massage parameters in a timely manner according to changes in the user's physical conditions, affecting the massage effect and comfort. Summary of the Invention

[0003] The present application provides a multifunctional health massage bed and a control system, aiming to solve the technical problems that existing health massage beds have a single function and cannot provide personalized physiotherapy solutions for users' physical conditions.

[0004] In view of the above problems, the present application provides a multifunctional health massage bed and a control system.

[0005] In the first aspect disclosed by the present application, there is provided a multifunctional health massage bed, including: a data monitoring module for accessing a plurality of health detection devices and acquiring a health monitoring data stream of a target user through the plurality of health detection devices; a solution analysis module for calling and obtaining a target physiotherapy solution classifier according to the physical signs information of the target user, performing solution analysis on the health monitoring data stream based on the target physiotherapy solution classifier, and determining a target physiotherapy solution; a multifunctional execution module for obtaining a massage bed function set, performing trigger parsing based on the target physiotherapy solution and the massage bed function set to obtain a target function control parameter solution, and performing massage monitoring on the target user through the target function control parameter solution to obtain massage monitoring feedback parameters; an early warning optimization module for evaluating health indicators of the massage monitoring feedback parameters, obtaining user health index information, and performing massage early warning optimization control on the target user based on the user health index information and the massage monitoring feedback parameters.

[0006] In another aspect disclosed by the present application, there is provided a control system for a multifunctional health massage bed. The system includes a memory and a processor. A control program is stored in the memory, and when the control program is executed by the processor, the module interaction of a multifunctional health massage bed as described in the first aspect is realized.

[0007] One or more technical solutions provided in the present application have at least the following technical effects or advantages: By adopting a data monitoring module to connect multiple health detection devices, and collecting the health monitoring data stream of the target user through the multiple health detection devices, the comprehensive collection and real-time monitoring of the user's health data are realized; through a scheme analysis module, which is used to call and obtain a target physiotherapy scheme classifier according to the physical signs information of the target user, and perform scheme analysis on the health monitoring data stream based on the target physiotherapy scheme classifier to determine the target physiotherapy scheme, so as to intelligently determine a personalized physiotherapy scheme according to the actual physical signs information of the user; through a multi-functional execution module, which is used to obtain the massage bed function set, perform trigger analysis based on the target physiotherapy scheme and the massage bed function set to obtain a target function control parameter scheme, and perform massage monitoring on the target user through the target function control parameter scheme to obtain massage monitoring feedback parameters, realizing the flexible call of the massage bed function and the dynamic control of parameters, and at the same time obtaining feedback parameters through the monitoring of the massage process; through an early warning optimization module, which performs health index evaluation on the massage monitoring feedback parameters to obtain the user's health index information, and performs massage early warning optimization control on the target user based on the user's health index information and the massage monitoring feedback parameters, realizing the real-time evaluation and optimization of the massage process, analyzing the user's health index, giving corresponding early warning prompts, and dynamically adjusting the massage parameters to ensure the massage effect. This technical solution solves the technical problems that the existing health care massage bed has a single function and cannot provide a personalized physiotherapy scheme according to the user's physical condition, and achieves the technical effect of intelligently determining a personalized physiotherapy scheme according to the user's health monitoring data stream, and at the same time realizing real-time monitoring and early warning optimization feedback during the massage process.

[0008] The above description is only an overview of the technical solution of this application. In order to be able to more clearly understand the technical means of this application, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of this application more obvious and understandable, the following specifically gives the specific implementation manners of this application. Brief Description of the Drawings

[0009] Figure 1 FIG. is a schematic structural diagram of a multi-functional health care massage bed provided by an embodiment of this application; Figure 2 FIG. is a schematic structural diagram of a control system of a multi-functional health care massage bed provided by an embodiment of this application.

[0010] Description of the reference numerals: data monitoring module 11, scheme analysis module 12, multi-functional execution module 13, early warning optimization module 14, memory 210, processor 220, control program 211. Detailed Description of the Invention

[0011] The overall idea of the technical solution provided by this application is as follows: The embodiments of the present application provide a multifunctional health massage bed and a control system. By setting up a data monitoring module, a scheme analysis module, a multifunctional execution module, and a warning and optimization module, it realizes the comprehensive collection and real-time monitoring of users' health data, intelligently determines personalized physiotherapy schemes according to the actual physical signs of users, and through the flexible invocation of the massage bed functions and the dynamic control of parameters, it monitors and gives feedback on the massage process in real time. At the same time, it evaluates and optimizes the massage process in real time, gives corresponding warning prompts, and dynamically adjusts the massage parameters to ensure the massage effect and safety. Specifically, the data monitoring module accesses multiple health detection devices to collect and obtain the health monitoring data stream of the target user; the scheme analysis module calls and obtains the target physiotherapy scheme classifier according to the physical signs of the target user, analyzes the health monitoring data stream for the scheme, and determines the target physiotherapy scheme; the multifunctional execution module obtains the massage bed function set, performs trigger parsing based on the target physiotherapy scheme and the massage bed function set to obtain the target function control parameter scheme, and performs massage monitoring on the target user through the target function control parameter scheme to obtain the massage monitoring feedback parameters; the warning and optimization module evaluates the health indicators of the massage monitoring feedback parameters to obtain the user health index information, and performs massage warning and optimization control on the target user based on the user health index information and the massage monitoring feedback parameters.

[0012] After introducing the basic principle of the present application, the following will specifically introduce various non-limiting implementation manners of the present application in conjunction with the accompanying drawings of the specification.

[0013] Embodiment 1, as Figure 1 shown, the embodiments of the present application provide a multifunctional health massage bed, and the method includes: A data monitoring module 11, configured to access multiple health detection devices, and collect and obtain the health monitoring data stream of the target user through the multiple health detection devices.

[0014] Specifically, the data monitoring module 11 is connected to multiple external health detection devices to realize the real-time monitoring of the health status of the target user. The data monitoring module 11 is connected to various types of health detection devices through a data interface, and these health detection devices include but are not limited to electrocardiographs, sphygmomanometers, thermometers, etc. Among them, the electrocardiograph detects physiological parameters such as the user's heart rate, blood pressure, blood oxygen saturation, respiratory rate, and electrocardiogram waveform; the sphygmomanometer measures the user's systolic blood pressure and diastolic blood pressure; the thermometer measures the user's body surface temperature or core body temperature. When these externally connected health detection devices work, they continuously collect the above-mentioned physiological index data of the user and transmit them in real time to form a dynamic health monitoring data stream. The data monitoring module 11 can receive the continuous health monitoring data stream of the target user transmitted in real time through the data transmission connection with these devices.

[0015] Through the data monitoring module 11, physiological health data such as the user's heart rate, blood pressure, and body temperature are comprehensively obtained for subsequent intelligent analysis of the physiotherapy plan. Using external professional health detection equipment for data collection can expand the source of health data of the massage bed, improve the comprehensiveness of monitoring, and at the same time, rely on the excellent performance of professional equipment to ensure the high precision and reliability of the collected user health data, thus providing a high-quality data basis for subsequent physiotherapy.

[0016] The plan analysis module 12 is used to call and obtain the target physiotherapy plan classifier according to the physical signs information of the target user, and perform plan analysis on the health monitoring data stream based on the target physiotherapy plan classifier to determine the target physiotherapy plan.

[0017] Specifically, the plan analysis module 12 first needs to obtain the physical signs information of the target user, including basic information such as the user's height, weight, age, and gender, as well as health background information such as the traditional Chinese medicine constitution classification, past medical history, and medication history of the target user. The plan analysis module 12 calls and matches the target physiotherapy plan classifier that best suits the user's personal characteristics from the physiotherapy plan classifier library according to this physical signs information of the user. Among them, multiple physiotherapy plan classifiers customized according to the physical signs characteristics of different populations are preset in the physiotherapy plan classifier library. Each physiotherapy plan classifier is obtained through learning and training of a large number of sample data of specific populations, and can perform intelligent classification and matching of physiotherapy methods according to the characteristics of health data. The plan analysis module 12 can obtain the target physiotherapy plan classifier that meets the individualized needs of the user through matching and retrieval of the user's physical signs information in the classifier library.

[0018] After obtaining the target physiotherapy plan classifier, the plan analysis module 12 applies it to the real-time health monitoring data stream of the target user collected and transmitted by the data monitoring module 11. This health monitoring data stream reflects the current objective health status of the target user. The target physiotherapy plan classifier uses machine learning algorithms to comprehensively analyze the health index data in this health monitoring data stream, find out the abnormal risk points reflected therein, and match the applicable physiotherapy methods according to the risk type, such as giving a blood pressure lowering massage plan for the data characteristics of high blood pressure, etc., so as to realize the intelligent mapping and generation from the user's health monitoring data to the physiotherapy plan. After the plan analysis module 12 inputs the health monitoring data stream into the intelligent analysis of the target physiotherapy plan classifier, it can determine the personalized target physiotherapy plan that meets the health needs of the target user as the target physiotherapy plan. This target physiotherapy plan includes a combination plan of targeted physiotherapy means such as massage, heat therapy, magnetic therapy, and negative oxygen ion therapy, as well as parameter settings of each physiotherapy means, such as the location, strength, and frequency of massage, and the temperature and duration of heat therapy.

[0019] By introducing a physiotherapy program classifier customized for different physical signs to conduct health data analysis and determine the design of the target physiotherapy program, the massage bed can customize a highly personalized and precise physiotherapy program according to the individual health characteristics of different users, thereby achieving better physiotherapy effects and enhancing the user's health improvement experience.

[0020] The multifunctional execution module 13 is used to obtain the massage bed function set, perform trigger analysis based on the target physiotherapy program and the massage bed function set, obtain the target function control parameter scheme, and perform massage monitoring on the target user through the target function control parameter scheme to obtain massage monitoring feedback parameters.

[0021] Specifically, the multifunctional execution module 13 first obtains the massage bed function set supported by the massage bed. The massage bed function set includes various physiotherapy functions integrated in the multifunctional health massage bed, including but not limited to music playback, heating, rhythmic massage, magnetic therapy, negative ion physiotherapy, etc. Each function has corresponding adjustable parameters, such as the track and volume of music playback, the temperature and duration of heating, the location and frequency of rhythmic massage, the intensity and distribution of the magnetic field, the concentration of negative ions, etc. The multifunctional execution module 13 summarizes these functions and parameters to form a complete massage bed function set. Next, the multifunctional execution module 13 performs trigger analysis on the target physiotherapy program output by the program analysis module 12 and the above massage bed function parameter set. The target physiotherapy program proposes a series of physiotherapy means for the user's health needs, such as massage, heat therapy, magnetic therapy, etc., and corresponding parameter requirements. The multifunctional execution module 13 finds the massage bed functions corresponding to the physiotherapy means required by the target physiotherapy program in the massage bed function set through analysis and triggers the activation of these functions.

[0022] Then, according to the parameter requirements of each physiotherapy method in the target physiotherapy plan, the multifunctional execution module 13 analyzes the optimal control parameter settings of each function from the massage bed function parameter set, and summarizes these parameters to generate a complete target function control parameter plan. This plan indicates the specific working parameter settings of each physiotherapy function of the massage bed, such as the rhythm frequency of rhythmic massage, the target temperature of heat therapy, etc., so that the working state of each function precisely matches the requirements of the target physiotherapy plan. After generating the target function control parameter plan, the multifunctional execution module 13 controls the massage bed to carry out physiotherapy work on the target user according to this parameter plan. For example, read the music playback track and volume settings in the target function control parameter plan, and control the audio system to play treatment music as required; read the heating temperature and time parameters, and control the operation of the heating component; read the rhythmic massage parameters, and control the massage mechanism to perform corresponding actions; read the magnetic therapy parameters, and control the magnetic field generating device to output a magnetic field with a specific intensity and distribution, etc. During the entire physiotherapy process, the multifunctional execution module 13 also constantly collects the working state feedback data of each function, such as parameters such as the actual output vibration frequency, heating temperature, and magnetic field intensity, and summarizes them to form massage monitoring feedback parameter data for subsequent physiotherapy effect evaluation and plan optimization.

[0023] Through the trigger analysis of the multifunctional execution module 13, a customized physiotherapy plan execution plan is formed, which fully exerts the physiotherapy performance of the massage bed. At the same time, through the monitoring feedback of the physiotherapy process, it provides a basis for the evaluation and control of the physiotherapy effect, ensuring the accuracy and controllability of the physiotherapy effect.

[0024] The warning and optimization module 14 is used to evaluate the health indicators of the massage monitoring feedback parameters, obtain the user health index information, and perform massage warning and optimization control on the target user based on the user health index information and the massage monitoring feedback parameters.

[0025] Specifically, the warning and optimization module 14 is responsible for analyzing and evaluating the massage monitoring feedback parameters provided by the multifunctional execution module 13, and then obtaining the user health index information. According to the health index and the massage monitoring feedback parameters, massage warning and optimization control are implemented on the target user.

[0026] During the massage and physiotherapy process, the warning and optimization module 14 receives in real time the massage monitoring feedback parameters from the multi-functional execution module 13. These parameters collect the actual working state data of each functional module during the massage process, including the actual frequency of rhythmic massage, the actual temperature of heat therapy, the actual magnetic field strength of magnetic therapy, etc. The warning and optimization module 14 comprehensively analyzes and evaluates these monitoring data to determine whether the actual working state of the massage bed meets the requirements of the target functional control parameter scheme, whether the physiotherapy intensity is appropriate, and whether there are any deviations or abnormalities. After completing the evaluation of the massage process, the warning and optimization module 14 quantifies the evaluation results using a mathematical model to generate user health index information representing the user's health status. This health index information is a multi-dimensional vector that comprehensively reflects the improvement effects of the user in multiple health dimensions such as nerves, circulation, respiration, metabolism, and immunity after this physiotherapy session.

[0027] Subsequently, based on the health index information and the massage monitoring feedback parameters, the warning and optimization module 14 can determine whether the current physiotherapy process is optimal and whether there is room for optimization. If it is found through analysis that some monitoring parameters are abnormal, such as too high heat therapy temperature or too strong magnetic field, which may harm the user's health, this module will promptly issue a warning signal to indicate that the physiotherapy intensity is too high and automatically adjust the relevant parameters to control the physiotherapy intensity back to the safe range, thus playing a role in risk warning and harm prevention. On the other hand, if the analysis shows that the current physiotherapy process is safe but the effect is not ideal and the room for improvement of the user's health index is limited, the warning and optimization module 14 will automatically optimize and adjust the functional control parameter scheme for the next round of physiotherapy, appropriately increasing the physiotherapy intensity or duration in order to obtain better health improvement effects. The optimized new parameter scheme is transmitted back to the multi-functional execution module 13 to guide the next round of physiotherapy operations, thereby forming a closed-loop feedback control to achieve continuous optimization and improvement of the physiotherapy process.

[0028] Through the warning and optimization module 14, warning judgment and optimization control are carried out based on objective massage monitoring data and quantitative health index evaluation, which can minimize the influence of human factors and achieve precise and intelligent adaptive control.

[0029] Furthermore, the embodiment of the present application further includes: Construct a massage bed physiotherapy database, classify and integrate the massage bed physiotherapy database according to the physical sign attribute information to obtain a physical sign attribute calibration parameter set; perform data clustering on the massage bed physiotherapy database based on the physical sign attribute calibration parameter set to obtain a physiotherapy attribute calibration data set; use a support vector machine to perform scheme classification training and parameter identification integration on the physiotherapy attribute calibration data set respectively to obtain a physiotherapy scheme classifier library; perform parameter matching and calling based on the physical sign information of the target user and the physiotherapy scheme classifier library to obtain the target physiotherapy scheme classifier.

[0030] In a feasible implementation, first, a physiotherapy database for a massage bed is constructed. A large amount of historical physiotherapy case data is stored in this database, and each piece of data includes the physical sign attribute information of the user, the adopted physiotherapy plan, and the corresponding physiotherapy effect feedback information, etc. The physical sign attribute information includes multi-dimensional physiological health characteristic parameters such as the user's age, gender, height and weight, traditional Chinese medicine constitution classification, and past medical history. These physical sign attributes reflect the user's health status and body characteristics from different angles. After the physiotherapy database for the massage bed is constructed, according to the physical sign attribute information in each historical case, the physiotherapy database for the massage bed is classified and integrated by attributes. The physiotherapy case data with similar physical sign attribute characteristics are grouped into the same category, forming a classification data set based on physical sign attributes. In this process, the multi-dimensional physical sign attribute parameters corresponding to each classification are extracted and summarized to form a set of physical sign calibration parameter sets. This set of physical sign calibration parameter sets reflects the physiological health characteristics of the typical population corresponding to each classification.

[0031] Next, based on the above-mentioned set of physical sign calibration parameter sets as the basis for data clustering, the physiotherapy database for the massage bed is clustered. That is, taking the set of physical sign calibration parameter sets corresponding to each physical sign classification as the clustering center, the physiotherapy data cases in the physiotherapy database for the massage bed that are most similar and closest to it are clustered together to form data clusters that are similar inside and different outside. For the physiotherapy cases within each data cluster, the physiological characteristics and health needs of the applicable population are highly similar, so the adopted physiotherapy plans also tend to be the same. These data clusters constitute the physiotherapy attribute calibration data set, and the distribution of physiotherapy plans within each cluster reflects the typical treatment needs of a specific population. Then, the support vector machine algorithm is used to perform scheme classification training and parameter identification integration on the above-mentioned physiotherapy attribute calibration data set respectively. For each calibrated data cluster, the support vector machine algorithm learns and extracts the mathematical characteristics and discrimination rules of various physiotherapy plans from it, establishes a classification decision model for the physiotherapy plan, and forms a physiotherapy plan classifier for this population. Training is carried out for each calibrated data cluster, so as to obtain multiple physiotherapy plan classifiers, which are summarized to form a physiotherapy plan classifier library. When the target user starts to use this multifunctional health massage bed, the specific physical sign information of the target user is obtained, and then based on the physical sign information of the target user, parameter matching and calling are performed with the physiotherapy plan classifier library to find the physiotherapy plan classifier that best matches the physical signs of the target user, and use it as the target physiotherapy plan classifier for the target user.

[0032] Furthermore, the embodiments of the present application also include: The target physiotherapy plan classifier analyzes the health monitoring data stream to obtain a matching physiotherapy plan; based on the physiotherapy attribute calibration data set, predicts the physiotherapy effect of the matching physiotherapy plan to determine physiotherapy effect prediction information; obtains the preset user required effect, and takes the difference between the physiotherapy effect prediction information and the preset user required effect as the physiotherapy effect deviation parameter; based on the physiotherapy effect deviation parameter, compensates and optimizes the matching physiotherapy plan to determine the target physiotherapy plan.

[0033] In a preferred implementation manner, first, the target physiotherapy plan classifier obtained by the plan analysis module 12 analyzes the health monitoring data stream from the data monitoring module 11. The target physiotherapy plan classifier is built with physiotherapy plan determination rules that conform to the health characteristics of the target user. These rules are applied to the analysis of the health monitoring data stream. According to the real-time health status information of the target user reflected in the data stream, one or several physiotherapy plans that are most suitable are quickly matched and screened out as the matching physiotherapy plans suitable for the current user's physical condition. Subsequently, the plan analysis module 12 searches in the physiotherapy attribute calibration data set for historical physiotherapy plans that are the same as or most similar to the matching physiotherapy plan, extracts the corresponding actual physiotherapy effect data as a reference sample for predicting the efficacy of the current matching physiotherapy plan, and then uses statistical learning and other methods to establish a physiotherapy effect prediction model to quantitatively analyze and calculate the expected effect of the matching physiotherapy plan, obtaining prediction information data reflecting the possible physiotherapy effect of the matching physiotherapy plan on the target user as the physiotherapy effect prediction information.

[0034] However, the physical therapy effect prediction information only represents the objective curative effect level of the matched physical therapy plan, which may have a certain gap with the subjective treatment needs of the target user. To meet the personalized needs of users to the greatest extent, the plan analysis module 12 obtains the user's preset ideal treatment effect requirement, that is, the preset user demand effect. After the plan analysis module 12 obtains the preset user demand effect, it compares it with the physical therapy effect prediction information, calculates the difference between the two, and obtains the physical therapy effect deviation parameter. This deviation parameter quantitatively reflects the gap between the expected effect of the matched physical therapy plan and the user's subjective demand. Then, the plan analysis module 12 compensates and optimizes the matched physical therapy plan based on the physical therapy effect deviation parameter to obtain the final target physical therapy plan. For example, if the physical therapy effect deviation parameter is positive, it indicates that the expected physical therapy effect cannot meet the user's needs. At this time, the plan analysis module 12 appropriately increases the key parameters in the matched physical therapy plan, such as increasing the massage duration, intensity, etc., to improve the physical therapy effect; if the physical therapy effect deviation parameter is negative, it indicates that the expected physical therapy effect has reached and exceeded the user's needs. At this time, the matched physical therapy plan can be maintained unchanged, or the key parameters can be appropriately reduced; if the deviation parameter is zero, it indicates that the expected physical therapy effect exactly meets the user's needs. At this time, the matched physical therapy plan is directly determined as the final target physical therapy plan. Through the compensation and optimization process, the target physical therapy plan achieves the best balance between the objective curative effect and the subjective demand.

[0035] Furthermore, the embodiments of the present application further include: Based on the target physical therapy plan and the massage bed function set, trigger function matching to activate the target massage bed application function; through the target massage bed application function, mine historical data to construct a massage bed function control space, which includes application function control parameters and corresponding control effect data; use the target physical therapy plan as a constraint parameter to perform matching and parsing in the massage bed function control space to obtain the selection threshold of the application function control parameters; perform global parameter optimization within the selection threshold of the application function control parameters to obtain the target function control parameter plan.

[0036] In a preferred embodiment, first, the multi-functional execution module 13 triggers function matching based on the target physiotherapy plan output by the plan analysis module 12 and the set of massage bed functions that the massage bed itself possesses. Specifically, the multi-functional execution module 13 compares each physiotherapy means required in the target physiotherapy plan, such as massage, heat therapy, music relaxation, etc., with each application function included in the set of massage bed functions one by one, finds one or more application functions corresponding to the target physiotherapy plan, and triggers and activates these target massage bed application functions to make them enter the working state and prepare to execute the physiotherapy task. This matching and triggering process realizes the mapping and allocation from the physiotherapy plan to the specific execution functions. Secondly, to further clarify the specific working parameter settings of each target massage bed application function, the multi-functional execution module 13 needs to perform historical data mining through these application functions to construct the massage bed function control space. The so-called historical data mining refers to analyzing the working data accumulated during the past actual operation of each application function, including the application function control parameters and the corresponding control effect data under various parameter settings. For example, for the massage function, its historical data includes the operation data under different parameter combinations of strength, frequency, duration, etc., and the effect data such as the actual achieved massage strength and speed under these parameter combinations. The multi-functional execution module 13 integrates the control effect data of each application function under all its parameter value combinations to form a multi-dimensional parameter-effect mapping space, that is, the massage bed function control space, which provides constraint conditions for subsequent parameter optimization.

[0037] Subsequently, the multi-functional execution module 13 uses the effect requirements for each application function in the target physiotherapy plan as constraint parameters and performs matching and analysis within the massage bed function control space. Specifically, the multi-functional execution module 13 extracts the quantitative or qualitative requirements for effect attributes such as strength and frequency in the target physiotherapy plan, and then uses these requirements as the matching targets to search for the control effect data points closest to them in the massage bed function control space, thereby determining the approximate selection range of the control parameters of each application function and obtaining a selection threshold for the application function control parameters. This threshold specifies the value range of each parameter. Adjusting the parameter settings within this range can make the actual control effect meet the requirements of the target physiotherapy plan. This matching and analysis process realizes the transformation from the target physiotherapy plan to the function parameter threshold. After that, under the constraint of the selection threshold of the application function control parameters, the multi-functional execution module 13 uses a global optimization algorithm to perform an exhaustive search within the multi-dimensional parameter space delimited by the parameter threshold to find the parameter combination with the optimal comprehensive control effect as the final target function control parameter plan.

[0038] Furthermore, the embodiment of the present application further includes: Based on the functional control effect target, perform an evaluation function fitting on the functional control space of the massage bed to construct a functional control effect fitness function; divide multiple solution sets for the selection threshold of the applied functional control parameters to obtain multiple control parameter selection solution sets; perform fitness evaluation on the multiple control parameter selection solution sets according to the functional control effect fitness function to obtain multiple parameter solution set fitness sets; perform parameter iterative optimization on the multiple control parameter selection solution sets based on the multiple parameter solution set fitness sets to obtain the target functional control parameter solution.

[0039] In a preferred embodiment, first, the multi-functional execution module 13 performs an evaluation function fitting on the functional control space of the massage bed based on the functional control effect target to construct a functional control effect fitness function. The so-called functional control effect target refers to the comprehensive effect indicators expected to be achieved by each functional module when performing the physiotherapy task, such as moderate massage strength, comfortable heat therapy temperature, gentle music rhythm, etc. The multi-functional execution module 13 uses these effect targets as dependent variables and the applied functional control parameters in the functional control space as independent variables, and uses machine learning algorithms such as neural networks to train and fit a fitness evaluation function with parameters as inputs and the degree of satisfaction of the effect targets as outputs. This function can quantitatively evaluate the closeness between the actual achieved comprehensive control effect and the ideal effect target under any set of control parameter settings, providing an evaluation criterion for parameter optimization. Then, for the obtained selection threshold of the applied functional control parameters, the multi-functional execution module 13 uses the interval division method to divide the threshold interval of each parameter into several non-overlapping sub-intervals, and then combines the sub-intervals of different parameters to obtain multiple control parameter selection solution sets. Each solution set represents a sub-region of the parameter space. The purpose of performing this solution set division is to first perform local optimization within each solution set during the parameter optimization process, and then perform global comparison between the local optimal solutions, thereby reducing the complexity of the optimization problem and improving the solution efficiency. The division granularity of the solution sets can be flexibly set according to actual needs. The more solution sets there are, the finer the granularity, the higher the optimization accuracy, but the larger the computational amount.

[0040] Subsequently, the multi-functional execution module 13 evaluates the fitness of each solution set of control parameter selections according to the constructed fitness function of the functional control effect. Specifically, one or more representative parameter combinations are selected from each solution set and substituted into the fitness function for calculation to obtain the fitness score of the comprehensive effect of the solution set, thereby obtaining a multi-parameter solution set fitness set containing the fitness scores of all solution sets. After that, the multi-functional execution module 13 performs iterative optimization on the solution sets of multiple control parameter selections based on the multi-parameter solution set fitness set. In each round of iteration, several solution sets with the lowest fitness scores are first eliminated, and then one or several solution sets with the highest fitness scores are selected from the remaining solution sets. The parameter spaces of these solution sets are divided and refined to form several new sub-solution sets, and the fitness of these sub-solution sets is evaluated according to the above method, and the evaluation results are added to the fitness set to update the set content for the next round of iteration. Iterate repeatedly until the number of solution sets or the granularity of the parameter space reaches the preset threshold. When the iteration terminates, the solution set with the highest score in the fitness set is the optimal solution set, which contains the parameter combination that best matches the effect target, and this combination is output as the final target functional control parameter scheme.

[0041] Further, the embodiment of the present application further includes: According to the multi-parameter solution set fitness set, calculate the total fitness of the solution sets of multiple control parameter selections; optimize the solution sets of multiple control parameter selections according to the total fitness of the multi-parameter solution sets to obtain the first solution set of control parameter selections; perform iterative division and fitness evaluation optimization on the first solution set of control parameter selections until the preset termination condition is reached to obtain the target solution set of control parameter selections; use the fitness function of the functional control effect to perform global optimization within the target solution set of control parameter selections to determine the target functional control parameter scheme.

[0042] In a preferred implementation manner, first, the multi-functional execution module 13 calculates the total fitness of the solution sets of each control parameter selection according to the multi-parameter solution set fitness set. Specifically, the sum of all fitness scores within each solution set is accumulated as the total fitness of the solution set of the parameter solution, thereby obtaining the total fitness of the multi-parameter solution sets. Secondly, the multi-functional execution module 13 optimizes the solution sets of multiple control parameter selections according to the above total fitness of the multi-parameter solution sets. Specifically, all solution sets are sorted from high to low according to their total fitness of the parameter solution sets, and the solution set with the highest total fitness is selected as the first solution set of control parameter selections, which represents the parameter space region with the best comprehensive effect at the current division granularity and is the focus of the next refinement optimization.

[0043] Subsequently, the multi-functional execution module 13 performs iterative partitioning and fitness evaluation optimization on the solution set of the first control parameter selection. Specifically, an iterative optimization method is adopted. The solution set of the first control parameter selection is used as the new optimization starting point, and its parameter space is subdivided into several sub-solution sets. Then, the fitness of each sub-solution set is evaluated according to the fitness function of the functional control effect. The solution set with the highest fitness is selected as the solution set of the first control parameter selection for the new round of iteration, and the partitioning and evaluation are performed again. Iterate in this way until the preset termination conditions are reached, such as the number of iteration rounds, the number of solution sets, or the solution set granularity exceeding the threshold. At this time, the optimal solution set selected in the last round of iterative evaluation is the solution set of the target control parameter selection. The parameter combinations in this solution set that best match the effect target in the entire parameter space are included, representing the global optimal solution at the current granularity. After that, the multi-functional execution module 13 uses the fitness function of the functional control effect to perform global optimization search within the parameter space range determined by the solution set of the target control parameter selection, and finds the parameter combination that maximizes the output value of the fitness function, and outputs it as the final target functional control parameter solution.

[0044] Furthermore, the embodiment of the present application further includes: Establish a health warning mechanism, trigger warning judgment and evaluate the warning level for the user's health index information based on the health warning mechanism to determine the health warning level; optimize and analyze the massage monitoring feedback parameters to obtain parameter optimization mutation rules; perform optimization mutation comparison on the target functional control parameter solution based on the parameter optimization mutation rules to obtain a functional control parameter optimization solution; perform massage warning optimization control on the target user based on the health warning level and the functional control parameter optimization solution.

[0045] In a preferred embodiment, first, the warning optimization module 14 establishes a health warning mechanism. This mechanism pre-sets a series of health risk assessment rules and warning level classification criteria. For example, the danger thresholds of physiological indicators such as heart rate and blood pressure are set as assessment rules, and the warning levels are classified into mild, moderate, severe, etc. according to the degree of deviation of the indicators from the thresholds. During the physiotherapy process, the warning optimization module 14 based on the above health warning mechanism, conducts real-time detection and evaluation of the user's health index information. Once it is found that a certain indicator exceeds the safety threshold, a corresponding level of warning is triggered, and at the same time, the specific degree of deviation of the indicator from the threshold is evaluated, thereby determining the health warning level to indicate the current health risk level of the user. Then, the warning optimization module 14 optimally analyzes the massage monitoring feedback parameters fed back by the multi-functional execution module 13, focuses on analyzing the feedback parameters related to the triggered warning, finds out the abnormal patterns and change rules therein, and summarizes and forms parameter optimization mutation rules. These rules describe the adjustment strategies that should be taken for each monitoring parameter when a warning occurs. For example, when the monitored value of the massage intensity is too large and the user's heart rate suddenly increases, the massage intensity should be immediately reduced and the rhythm should be slowed down, etc. The parameter optimization mutation rules, through machine learning of the monitoring data, excavate the association model between the physiotherapy parameters and the user's physiological state, which is convenient for subsequent implementation of targeted parameter optimization.

[0046] Subsequently, the warning optimization module 14 conducts an optimized mutation comparison of the target function control parameter scheme currently being executed based on the parameter optimization mutation rules. This module substitutes the current control parameter settings into the parameter optimization mutation rules for simulation calculation, obtains a series of optimized mutation parameter settings, and then compares these mutation settings with the current scheme to evaluate their effectiveness in alleviating the warning risk. Finally, the optimal parameter optimization mutation scheme is selected as the result of this round of warning optimization, denoted as the function control parameter optimization scheme. Compared with the current control scheme, this optimization scheme minimizes the warning risk while ensuring the physiotherapy effect. After that, the warning optimization module 14 comprehensively considers the health warning level and the function control parameter optimization scheme, and implements dynamic massage warning optimization control for the target user. When the warning level is relatively low, only the part of the parameters in the function control parameter optimization scheme targeted at the corresponding risk is adjusted and sent down to the multi-functional execution module 13 to dynamically adjust the physiotherapy intensity in a local optimization manner to alleviate the mild risk; when the warning level is relatively high, the complete function control parameter optimization scheme is sent down to timely change the physiotherapy mode in a global optimization manner to cope with the severe risk; if the warning level reaches the danger threshold, the multi-functional execution module 13 is immediately notified to terminate the current physiotherapy, and a warning is sent to the user in a timely manner to control the risk within the safety line. Through the hierarchical warning optimization control strategy, the massage bed can intelligently adjust the physiotherapy state according to the risk level, dynamically balance health safety and physiotherapy effect, and achieve timely risk warning and proper handling.

[0047] In summary, the multifunctional health care massage bed provided by the embodiments of the present application has the following technical effects: A data monitoring module is used to connect to multiple health detection devices, collect and obtain the health monitoring data stream of the target user through the multiple health detection devices, and provide a data basis for subsequent scheme analysis and early warning optimization. A scheme analysis module is used to call and obtain a target physiotherapy scheme classifier according to the physical signs information of the target user, and perform scheme analysis on the health monitoring data stream based on the target physiotherapy scheme classifier to determine the target physiotherapy scheme, so as to intelligently determine a personalized target physiotherapy scheme. A multifunctional execution module is used to obtain the massage bed function set, perform trigger parsing based on the target physiotherapy scheme and the massage bed function set to obtain a target function control parameter scheme, and perform massage monitoring on the target user through the target function control parameter scheme to obtain massage monitoring feedback parameters, realizing real-time monitoring and dynamic control of the massage process. An early warning optimization module is used to evaluate the health indicators of the massage monitoring feedback parameters, obtain user health index information, and perform massage early warning optimization control on the target user based on the user health index information and the massage monitoring feedback parameters, realizing real-time evaluation, early warning prompt and parameter optimization of the massage process, and ensuring the massage effect and safety.

[0048] Embodiment 2. Please refer to Figure 2 , Figure 2 which is a schematic diagram of an embodiment of a control system of a multifunctional health care massage bed provided by an embodiment of the present invention. As Figure 2 shown, this embodiment provides a control system of a multifunctional health care massage bed. The control system of the multifunctional health care massage bed includes a memory 210 and a processor 220. A control program 211 is stored in the memory 210. When the control program is executed by the processor 220, it can realize the module interaction of a multifunctional health care massage bed.

[0049] Any step of the method described above can be stored as a computer instruction or program in an unrestricted computer memory and can be called and recognized by an unrestricted computer processor to implement any method in the embodiments of the present application. No redundant restrictions are made here.

[0050] Furthermore, the first or second mentioned above may not only represent an order relationship, but may also represent a certain specific concept, and / or refer to the selection of multiple elements individually or in whole. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to include these changes and modifications.

Claims

1. A multifunctional health massage bed, characterized in that: The massage bed comprises: A data monitoring module is used to access multiple health detection devices and collect health monitoring data streams of target users through the multiple health detection devices; A program analysis module, configured to acquire a target therapy program classifier according to the physical sign information of the target user, and perform program analysis on the health monitoring data stream based on the target therapy program classifier to determine a target therapy program; a multifunctional execution module, for obtaining a massage bed function set, performing trigger analysis based on the target physiotherapy scheme and the massage bed function set, obtaining a target function control parameter scheme, and performing massage monitoring on the target user through the target function control parameter scheme to obtain massage monitoring feedback parameters; The early warning optimization module is used to perform health index evaluation on the massage monitoring feedback parameters, obtain user health index information, and perform massage early warning optimization control on the target user based on the user health index information and the massage monitoring feedback parameters.

2. A multifunctional health care massage bed as claimed in claim 1, characterized in that: The step of calling and acquiring a target physical therapy program classifier according to the physical sign information of the target user includes: Constructing a massage bed therapy database, classifying and integrating the massage bed therapy database according to the physical sign attribute information, and obtaining a physical sign attribute calibration parameter set; Performing data clustering on the massage bed physiotherapy database based on the physical sign attribute calibration parameter set to obtain a physiotherapy attribute calibration data set; A support vector machine is used to perform scheme classification training and parameter identification integration on the physiotherapy attribute calibration data set to obtain a physiotherapy scheme classifier library; Based on the physical sign information of the target user and the physical therapy program classifier library, parameter matching and calling are performed to obtain the target physical therapy program classifier.

3. A multifunctional health care massage bed as claimed in claim 2, characterized in that: The target physical therapy program includes: Performing a scheme analysis on the health monitoring data stream by using the target therapy scheme classifier to obtain a matching therapy scheme; Predicting the physical therapy effect of the matching physical therapy plan based on the physical therapy attribute calibration data set to determine physical therapy effect prediction information; Obtaining a preset user demand effect, and using the difference between the physiotherapy effect prediction information and the preset user demand effect as a physiotherapy effect deviation parameter; The matching physical therapy plan is compensated and optimized based on the physical therapy effect deviation parameter to determine the target physical therapy plan.

4. A multifunctional health care massage bed as claimed in claim 1, characterized in that: The scheme for obtaining the target function control parameter comprises: Perform function matching triggering based on the target physiotherapy program and the massage bed function set, and activate the target massage bed application function; Perform historical data mining through the target massage bed application function to construct a massage bed function control space, wherein the massage bed function control space includes application function control parameters and corresponding control effect data; The target physiotherapy program is used as a constraint parameter to perform matching analysis in the massage bed function control space to obtain an application function control parameter selection threshold; Perform global optimization of parameters within the application function control parameter selection threshold to obtain the target function control parameter solution.

5. A multifunctional health care massage bed as claimed in claim 4, characterized in that: The method of obtaining the target function control parameter scheme includes: Based on the functional control effect target, an evaluation function is fitted to the functional control space of the massage bed to construct a functional control effect fitness function; Dividing the application function control parameter selection threshold into multiple solution sets to obtain multiple control parameter selection solution sets; Performing fitness evaluation on the plurality of control parameter selection solution sets according to the functional control effect fitness function to obtain a plurality of parameter solution set fitness sets; Based on the fitness sets of the multiple parameter solution sets, the multiple control parameter selection solution sets are subjected to parameter iterative optimization to obtain the target function control parameter solution.

6. A multifunctional health care massage bed as claimed in claim 5, characterized in that: The method of obtaining the target function control parameter scheme includes: According to the plurality of parameter solution set fitness sets, a sum of the plurality of parameter solution set fitnesses of the plurality of control parameter selection solution sets is calculated; Optimizing the multiple control parameter selection solution sets according to the sum of the fitness of the multiple parameter solution sets to obtain a first control parameter selection solution set; Iteratively partitioning and optimizing the first control parameter selection solution set through fitness evaluation until a preset termination condition is met, thereby obtaining a target control parameter selection solution set; The functional control effect fitness function is used to perform global optimization in the target control parameter selection solution set to determine the target functional control parameter solution.

7. A multifunctional health care massage bed as claimed in claim 1, characterized in that: The early warning optimization of the target function control parameter scheme based on the user health index information includes: Establishing a health warning mechanism, and conducting warning trigger judgment and warning degree assessment on the user's health index information based on the health warning mechanism to determine the health warning level; Optimizing and analyzing the massage monitoring feedback parameters to obtain parameter optimization variation rules; Based on the parameter optimization variation rule, the target function control parameter scheme is optimized and compared to obtain a function control parameter optimization scheme; Massage warning optimization control is performed on the target user based on the health warning level and the function control parameter optimization scheme.

8. A multifunctional health massage bed control system, characterized in that: The control system includes a memory and a processor, wherein a control program is stored in the memory, and when the control program is executed by the processor, the module interaction of the multifunctional health massage bed according to any one of claims 1 to 7 can be realized.

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